首页|期刊导航|分布式能源|机会约束规划下的区域综合能源系统多时间尺度低碳调度方法

机会约束规划下的区域综合能源系统多时间尺度低碳调度方法OA

A Multi-Time-Scale Low-Carbon Scheduling Method for Regional Integrated Energy Systems Under Chance-Constrained Programming

中文摘要英文摘要

风力发电与光伏发电的不确定性,导致低碳调度方法的碳排放量与预期不符,因此,提出一种机会约束规划下的区域综合能源系统多时间尺度低碳调度方法.首先,以风光发电不同时段的功率作为随机变量,并引入置信水平量化约束,利用改进粒子群算法,确定最优决策变量;其次,引入碳捕集电厂对 CO2 进行捕集、封存与再利用,构建碳循环体系,设计阶梯式碳交易机制与用户需求响应机制;最后,设计多时间尺度实时滚动调控计划,构建实时调度目标函数与约束条件,实现区域综合能源系统的多时间尺度低碳调度.实验结果表明,所设计的调度方法的碳排放量相较无策略场景减少了 4 570.1 kg,且实际碳排放略低于无偿配额 5%,可在有效利用低碳资源的同时,满足系统碳排放约束的要求.

The uncertainty of wind and photovoltaic power generation results in carbon emissions of low-carbon scheduling methods not meeting expectations.Therefore,a multi-time-scale low-carbon scheduling method for regional integrated energy systems under chance constrained planning is proposed.Firstly,it uses the power of wind and solar power generation at different time periods as random variables,and introduces confidence level quantification constraints,an improved particle swarm algorithm is used to determine the optimal decision variables.Secondly,it introduces carbon capture power plants to capture,store,and reuse CO2,constructs a carbon cycle system,and designs a tiered carbon trading mechanism and user demand response mechanism.Finally,it designs a multi-time-scale real-time rolling control plan,constructs real-time scheduling objective functions and constraints,and achieves low-carbon scheduling of regional integrated energy systems at multiple time scales.The experimental results show that the designed scheduling method reduces carbon emissions by 4 570.1 kg compared to the no strategy scenario,and the actual carbon emissions are slightly lower than the free quota by 5%.It can effectively utilize low-carbon resources while meeting the requirements of system carbon emission constraints.

陈智祺;吴方权;李康

贵州电网有限责任公司电力调度控制中心,贵州省 贵阳市 550000贵州电网有限责任公司数智运营中心,贵州省 贵阳市 550000贵州电网有限责任公司安顺供电局,贵州省 安顺市 561000

能源科技

机会约束规划区域综合能源系统多时间尺度源荷不确定粒子群优化算法

chance-constrained programmingregional integrated energy systemmulti-time-scalesource-load uncertaintyparticle swarm optimization algorithm

《分布式能源》 2026 (3)

110-118,9

This work is supported by Science and Technology Project of Guizhou Power Grid Co.,Ltd.(No.GZKJXM20232381). 贵州电网有限责任公司科技项目(GZKJXM20232381)

10.16513/j.2096-2185.DE.25100315

评论